Listar por autor "Marín-Jiménez, Manuel J."
Mostrando ítems 1-9 de 9
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A cross-dataset deep learning-based classifier for people fall detection and identification
Delgado-Escaño, Rubén; Castro, Francisco M.; Ramos-Cózar, Julián
; Marín-Jiménez, Manuel J.; Guil-Mata, Nicolás; Casilari-Pérez, Eduardo[et al.] (Elsevier, 2020)
This paper addresses the issue of fall detection, particularly for elderly individuals who may live alone and be unable to call for help after a fall. The objective is to develop a deep learning-based approach that can ... -
End-to-end Incremental Learning
Castro Payán, Francisco Manuel; Marín-Jiménez, Manuel J.; Guil-Mata, Nicolás; Schmid, Cordelia; Alahari, Karteek (2018-07-06)
Although deep learning approaches have stood out in recent years due to their state-of-the-art results, they continue to suffer from (catastrophic forgetting), a dramatic decrease in overall performance when training with ... -
Evaluation of CNN architectures for gait recognition based on optical flow maps
Castro, Francisco M.; Marín-Jiménez, Manuel J.; Guil-Mata, Nicolás; López-Tapia, S.; Pérez de la Blanca, N. (2017)
This work targets people identification in video based on the way they walk (\ie gait) by using deep learning architectures. We explore the use of convolutional neural networks (CNN) for learning high-level descriptors ... -
Gait recognition and fall detection with inertial sensors
Delgado-Escaño, Rubén; Castro, Francisco M.; Marín-Jiménez, Manuel J.; Guil-Mata, Nicolás (2019-11-26)
In contrast to visual information that is recorded by cameras placed somewhere, inertial information can be obtained from mobile phones that are commonly used in daily life. We present in this talk a general deep learning ... -
Gait recognition applying Incremental learning
Castro, Francisco M.; Marín-Jiménez, Manuel J.; Guil-Mata, Nicolás; Schmid, Cordelia; Alahari, Karteek (2019-11-25)
when new knowledge needs to be included in a classifier, the model is retrained from scratch using a huge training set that contains all available information of both old and new knowledge. However, in this talk, we present ... -
Multimodal feature fusion for CNN-based gait recognition: an empirical comparison
Castro, Francisco M.; Marín-Jiménez, Manuel J.; Guil-Mata, Nicolás; Pérez de la Blanca, Nicolás (Springer London, 2020)
This paper focuses on identifying people based on their gait using a non-invasive approach. Traditional methods rely on gait signatures derived from binary energy maps, which introduce noise. Instead, the authors explore ... -
Multimodal features fusion for gait, gender and shoes recognition
Castro Payán, Francisco Manuel; Marín-Jiménez, Manuel J.; Guil-Mata, Nicolás (Springer, 2016)
This paper evaluates how fusing multimodal features (audio, RGB, and depth) can enhance the task of gait recognition, as well as gender and shoe recognition. While most previous research has focused on visual descriptors ... -
On how to improve tracklet-based gait recognition systems
Marín-Jiménez, Manuel J.; Castro Payán, Francisco Manuel; Carmona-Poyato, Ángel; Guil-Mata, Nicolás (Elsevier, 2015-12-15)
Abstract Recently, short-term dense trajectories features (DTF) have shown state-of-the-art results in video recognition and retrieval. However, their use has not been extensively studied on the problem of gait recognition. ... -
A weakly-supervised approach for discovering common objects in airport video surveillance footage
Castro Payán, Francisco Manuel; Delgado-Escaño, Rubén; Guil-Mata, Nicolás; Marín-Jiménez, Manuel J. (2019-07-22)
Object detection in video is a relevant task in computer vision. Standard and current detectors are typically trained in a strongly supervised way, what requires a huge amount of labelled data. In contrast, in this paper ...